{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T10:40:56Z","timestamp":1777286456383,"version":"3.51.4"},"reference-count":29,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Delta Power Electronics Science and Education Development Program of Delta Group","award":["DREK2021003"],"award-info":[{"award-number":["DREK2021003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As industrial development increases, electric machine systems are more widely used in industrial production. Rolling bearings play a key role in machine systems and so the prevention of faults in rolling bearings is more important than ever before. Recently, with the development of artificial intelligence, neural networks have been used to monitor the remaining useful life of rolling bearings. However, there are two problems with this technique. First, a network trained by data for a single operating condition (source domain) cannot predict the remaining useful life of bearings under a different operating condition (target domain), such as a different load or speed. Second, a large number of labeled data are needed for network training, but the acquisition of labeled data for different operating conditions is a challenging task. To address these problems, this paper proposes a domain-adaptive adversarial network, in which a transfer learning strategy and maximum mean discrepancy algorithm are used for network optimization, so that remaining useful life can be predicted without labeled data in target domain training. Our results confirm that a model trained by source domain data alone cannot predict the remaining useful life of bearings under different conditions, but the domain-adaptive adversarial network can accurately predict remaining useful life for varying operating conditions. The method proposed also exhibits good performance even if there are noises in the signals.<\/jats:p>","DOI":"10.3390\/s23010227","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T03:05:56Z","timestamp":1672110356000},"page":"227","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Method for Predicting RUL of Rolling Bearings under Different Operating Conditions Based on Transfer Learning and Few Labeled Data"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2279-6500","authenticated-orcid":false,"given":"Wei","family":"Sun","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Haowen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2342-2340","authenticated-orcid":false,"given":"Zicheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}]},{"given":"Ronghai","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.measurement.2013.11.012","article-title":"Condition monitoring and fault diagnosis of planetary gearboxes: A review","volume":"48","author":"Lei","year":"2014","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.ymssp.2013.01.010","article-title":"A nonlinear probabilistic method and contribution analysis for machine condition monitoring","volume":"37","author":"Yu","year":"2013","journal-title":"Mech. 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